Question: How do sets of features derived from different dMRI processing methods compare in model accuracy and variability?
R and trained using nested group cross-validation and boostrap resampling by family.
Data were provided by the Human Connectome Project, WU-Minn Consortium.
Poster Github
Model accuracies for behavioral phenotypes. All models, regardless of regularization method or feature set performed equivalently. These \(R^2\) values are in line with previous literature evaluating brain-behavior predictive models.(3)
“Age” prediction model weights across tracts for SGL and LASSO. Solid lines show the mean model weight across bootstraps for every tract, across every node. The shaded areas show the 95% confidence intervals of the model weights. Note the reduced y-axis range for SGL. This pattern was consistent across phenotypes.